A method, system, readable storage medium and program product for structural design optimization of a sealing gasket waterproof performance based on joint simulation

Abstract

This invention belongs to the field of gasket design and relates to a method for optimizing the structural design of gasket waterproofing performance based on co-simulation. The method includes: parametrically modeling the gasket using Abaqus; performing finite element analysis on the parametric geometric model to obtain the mechanical response data of the gasket; adding the Abaqus component to Isight; extracting the mechanical response data document from the ODB result file output by Abaqus using a Python script; adding the Simcode component to Isight, with Isight driving the Python script to output the specific average contact stress data from the mechanical response data document output by Abaqus simulation, and adding it to the results of each simulation; adding the Optimization module integrating a multi-island genetic optimization algorithm to Isight, and optimizing the structural parameters of the gasket based on the multi-island genetic algorithm; the optimization objective is to maximize the average contact stress; and outputting the optimal structural design parameters.

Description

Technical Field

[0001] This invention belongs to the field of gasket design, and more specifically, relates to a method for optimizing the structural design of gasket waterproof performance based on co-simulation. Background Technology

[0002] Water leakage is the most common problem in shield tunnels, and the joints between tunnel segments are the weakest link in the waterproofing system. Their waterproofing performance directly affects the stability and service life of the entire tunnel. Therefore, researching and improving the waterproofing performance of sealing gaskets is key to solving this long-standing problem that has plagued the engineering community.

[0003] Currently, many researchers conduct gasket simulation experiments by manually adjusting the seals to find the optimal structural design. This manual method not only consumes a lot of manpower and resources, but the best result found is usually a relatively good value rather than the optimal value.

[0004] Therefore, there is a need for an optimization module that combines Isight with Abaqus for simulation analysis of hyperelastic materials, and introduces a multi-island genetic algorithm to automatically solve the optimization design of the sealing gasket structure. This would maximize the waterproof performance while meeting structural requirements, saving labor and time costs, and improving accuracy. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method for optimizing the structural design of waterproof performance of sealing gaskets based on co-simulation. The purpose is to solve the technical problems of low efficiency and difficulty in finding the optimal design in the existing technology of manual simulation experiments.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for optimizing the structural design of waterproof performance of sealing gaskets based on co-simulation is provided, comprising the following steps: Step S1: Use the finite element analysis software Abaqus to perform parametric modeling of the gasket, set the hole position coordinates and hole size parameters of the gasket as adjustable design parameters, and obtain the parametric geometric model of the gasket. Step S2: Mesh the parametric geometric model, apply boundary conditions and loads, perform finite element analysis, and obtain the mechanical response data of the sealing gasket; Step S3: Add the Abaqus component to Isight, select the design parameters of the parametric geometric model, read the Abaqus CAE file through Isight, and control the automatic operation and solution of Abaqus. Step S4: Automatically extract the mechanical response data document from the ODB result file output by Abaqus using a Python script. The mechanical response data document includes the average contact stress data for the specified analysis step and the specified node set. Step S5: Add the Simcode component, which integrates a Python batch processing program, to Isight. Isight drives the Python script in step S4 to run by driving the Python batch processing file, and automatically outputs the specific average contact stress data in the mechanical response data document output by Abaqus simulation each time step S4 is executed, and adds it to the results of each simulation. Step S6: Add the Optimization module, which integrates a multi-island genetic optimization algorithm, to Isight. Determine the population of individuals for the multi-island genetic algorithm, consisting of the design parameters from Step S1, the number of islands and the number of generations, the algorithm parameters, and the optimization objective. Optimize the structural parameters of the sealing gasket based on the multi-island genetic algorithm. The optimization objective is to maximize the average contact stress obtained in Step S5. Step S7: Run the Optimization module to calculate the optimal solution of the design parameters that maximize the average contact stress, and output the optimal structural design parameters.

[0007] Furthermore, the design parameters include the radius of the sealing gasket holes, the hole spacing, and the hole edge distance.

[0008] Furthermore, in step S2, the sealing gasket contact surface is defined as FACE1 in Abaqus, and a node set of FACE1 is generated so that it can be directly extracted by Python in step S4.

[0009] Further, step S4 includes: S41: Extract the ODB result file of the current Abaqus simulation output in step S3; S42: Obtain the node set FACE1 from the ODB result file output by Abaqus; S43: Extract the average contact stress avg_mises obtained from this Abaqus simulation from the node set FACE1.

[0010] Furthermore, in step S6, the optimization process of the multi-island genetic algorithm is as follows: a specified number of design parameter combinations are randomly generated, each combination is equivalent to an individual, these individuals are randomly assigned to multiple islands, the population of each island is independently subjected to selection, crossover and mutation operations, and a few elite individuals on each island are periodically allowed to migrate to other islands, thereby gradually iterating to find the global optimal solution.

[0011] Furthermore, in the optimization process of the multi-island genetic algorithm, maximizing the average contact stress mises is taken as the optimization objective. The subpopulation size, number of islands, number of generations, total number of iterations, crossover probability, and mutation probability are set, and then iterative optimization is performed.

[0012] According to another aspect of the present invention, a computer device system is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement a method for optimizing the structural design of waterproof gaskets based on co-simulation as described in any of the preceding claims.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a method for optimizing the structural design of a gasket's waterproof performance based on co-simulation as described in any of the preceding claims.

[0014] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements a method for optimizing the structural design of a gasket based on co-simulation for waterproof performance as described in any of the preceding claims.

[0015] In summary, the technical solutions conceived in this invention, compared with the prior art, can achieve the following beneficial effects: This invention combines Isight's optimization module with Abaqus finite element simulation analysis and introduces a multi-island genetic algorithm to optimize the structure of gaskets and improve waterproofing performance. Compared to traditional gasket structure design by researchers through extensive experiments, this method significantly simplifies the engineering workload, reduces manual intervention, and noticeably improves optimization results.

[0016] The technical solution of this invention is not only applicable to the optimization of gasket pore structure parameters, but can also be extended to the structural design optimization of other types of rubber seals. By modifying the design variables and optimization objectives of the parametric model, it can adapt to the gasket design requirements under different working conditions, demonstrating good versatility and engineering application value. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall process of a method for optimizing the waterproof performance structure of a sealing gasket based on co-simulation, according to a preferred embodiment of the present invention. Figure 2 The Isight optimization flowchart is a preferred embodiment of the present invention for optimizing the structural design of waterproof gaskets based on co-simulation. Figure 3 This is a schematic diagram of the gasket cross-section before optimization, according to a preferred embodiment of the present invention, which is a gasket waterproof performance structural design optimization method based on co-simulation. Figure 4 This is a schematic diagram of the cross-section of a sealing gasket after optimization, according to a preferred embodiment of the present invention, which is a structural design optimization method for waterproof performance of sealing gaskets based on co-simulation. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0019] Please see Figures 1-4 This invention provides a solution: a method for optimizing the structural design of waterproof gaskets based on co-simulation, comprising the following steps: Step S1: Parametrically model the gasket using the finite element analysis software Abaqus. Set the coordinates of the hole position and the hole size parameters of the gasket as adjustable design parameters. The design parameters include the gasket hole radius r, hole spacing s1, s2, and hole edge distance s3. The value range of the variables is as follows: r=[2,3],s1=[4,6],s2=[8,12],s3=[14,16]; Each iteration of the solution generates new optimization parameters; Step S2: Mesh the parametric geometric model in Abaqus, apply boundary conditions and loads, perform finite element analysis, and obtain the mechanical response data of the gasket. In Abaqus, define the gasket contact surface as FACE1 and generate the node set of FACE1. The calculation conditions in this embodiment are as follows: the upper pipe segment is compressed by 10mm, and a surface load of 0.4MPa is applied to the water-facing surface of the gasket to simulate water pressure. Step S3: Add the Abaqus component to Isight and select the model's design parameters. Preferably, in this embodiment, the design parameters are the sealing gasket hole radius r, the upper row hole spacing s1, the lower row hole spacing s2, and the hole edge distance s3. Isight reads the Abaqus CAE file, controls the automatic operation and solution of Abaqus, and realizes automatic reading and writing of design parameters and data output. In other embodiments, the design parameters can also be set according to the actual situation. For example, if there is only a single row of holes, then there is only one hole spacing parameter.

[0020] Step S4: Automatically extract mechanical response data (preferably waterproof performance evaluation index data) from the ODB result file output by Abaqus using a Python script. The Python script calls the Python API interface provided by Abaqus to read the ODB file and extract the average contact stress data for the specified analysis step and specified node set. The Python script is as follows: from odbAccess import * import numpy as np odb = openOdb('Job-1.odb', readOnly=True) # Get the node set FACE1 nodeSet = odb.rootAssembly.nodeSets['FACE1'] step = odb.steps.values()[-1] frame = step.frames[-1] # Stress Extraction stress = frame.fieldOutputs['S'].getSubset(region=nodeSet) mises = [v.mises for v in stress.values] avg_mises = np.mean(mises) with open("result.txt", "w") as f: f.write(str(avg_mises)) odb.close() The operation steps represented by the above code are as follows: S41: Extract the ODB result file of the current Abaqus simulation output in step S3; S42: Obtain the node set FACE1 from the ODB result file output by Abaqus; S43: Extract the average contact stress avg_mises obtained from this Abaqus simulation from the node set FACE1.

[0021] Step S5: Add the Simcode component, which integrates a Python batch processing program, to Isight. Isight drives the Python script to run by driving the Python batch processing file and automatically outputs a result file containing the average contact stress. Step S6: Add the Optimization module, which integrates the multi-island genetic optimization algorithm, to Isight. The multi-island genetic algorithm is an improved genetic algorithm based on parallel computing. Its core is to divide a large population into multiple relatively independent subpopulations (islands). Each island runs a standard genetic algorithm independently, and after a certain number of generations, a small number of individuals are "migrated" between different islands. In this embodiment, the multi-island genetic algorithm randomly generates a large number of design parameter combinations (sealing gasket hole radius r, hole spacing s1, s2, hole edge distance s3). Each combination is equivalent to an individual. These individuals are randomly assigned to multiple islands. Each island's population independently applies genetic operations such as selection, crossover, and mutation to evolve towards better performance. The multi-island genetic algorithm periodically allows a small number of elite individuals from each island to migrate to other islands. These elite individuals, carrying superior genes (design parameter combinations), can help the entire algorithm escape local optima and find the global optimum after hybridization with other populations. The optimization objective was to maximize the average contact stress (mises). The subpopulation size was set to 10, the number of islands to 10, the number of generations to 10, the total number of iterations to 1000, the crossover probability to 0.9, and the mutation probability to 0.1. The structural parameters of the sealing gasket were optimized based on the aforementioned multi-island genetic algorithm. Step S7: Run the calculation to obtain the optimal solution and output the optimal structural design parameters. The final optimization result of this embodiment is as follows: Figure 3 As shown, r=2.30, s1=4.74, s2=11.18, s3=14.67, compared to the original sealing gasket model (r=2.50, s1=5.00, s2=10.00, s3=15.00)... Figure 4 As shown, the optimized gasket model has a smaller aperture, a larger solid portion, and increased structural stiffness, resulting in a higher average contact stress under the same compression. The optimized model also reduces the horizontal distance between the upper and lower holes, leading to a more concentrated distribution of some holes. This shortens and nearly verticalizes the path of the compressive load from the upper row of holes to the lower row and then to the contact surface, reducing lateral load diffusion and attenuation and allowing more stress to act directly on the contact area. The final optimized model achieves an average surface contact stress of 1.26 MPa, approximately 33% higher than the original model's 0.95 MPa.

[0022] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the structural design of waterproof gaskets based on co-simulation, characterized in that, Includes the following steps: Step S1: Use the finite element analysis software Abaqus to perform parametric modeling of the gasket, set the hole position coordinates and hole size parameters of the gasket as adjustable design parameters, and obtain the parametric geometric model of the gasket. Step S2: Mesh the parametric geometric model, apply boundary conditions and loads, perform finite element analysis, and obtain the mechanical response data of the sealing gasket; Step S3: Add the Abaqus component to Isight, select the design parameters of the parametric geometric model, read the Abaqus CAE file through Isight, and control the automatic operation and solution of Abaqus. Step S4: Automatically extract the mechanical response data document from the ODB result file output by Abaqus using a Python script. The mechanical response data document includes the average contact stress data for the specified analysis step and the specified node set. Step S5: Add the Simcode component, which integrates a Python batch processing program, to Isight. Isight drives the Python script in step S4 to run by driving the Python batch processing file, and automatically outputs the specific average contact stress data in the mechanical response data document output by Abaqus simulation each time step S4 is executed, and adds it to the results of each simulation. Step S6: Add the Optimization module, which integrates a multi-island genetic optimization algorithm, to Isight. Determine the population of individuals for the multi-island genetic algorithm, consisting of the design parameters from Step S1, the number of islands and the number of generations, the algorithm parameters, and the optimization objective. Optimize the structural parameters of the sealing gasket based on the multi-island genetic algorithm. The optimization objective is to maximize the average contact stress obtained in Step S5. Step S7: Run the Optimization module to calculate the optimal solution of the design parameters that maximize the average contact stress, and output the optimal structural design parameters.

2. The method for optimizing the structural design of waterproof gaskets based on co-simulation according to claim 1, characterized in that, The design parameters include the radius of the sealing gasket holes, the hole spacing, and the hole edge distance.

3. The method for optimizing the structural design of waterproof gaskets based on co-simulation according to claim 2, characterized in that, In step S2, the sealing gasket contact surface is defined as FACE1 in Abaqus, and a node set of FACE1 is generated so that it can be directly extracted by Python in step S4.

4. The method for optimizing the structural design of waterproof gaskets based on co-simulation according to claim 3, characterized in that, Step S4 includes: S41: Extract the ODB result file of the current Abaqus simulation output in step S3; S42: Obtain the node set FACE1 from the ODB result file output by Abaqus; S43: Extract the average contact stress avg_mises obtained from this Abaqus simulation from the node set FACE1.

5. The method for optimizing the structural design of waterproof gaskets based on co-simulation according to claim 3, characterized in that, In step S6, the optimization process of the multi-island genetic algorithm is as follows: a specified number of design parameter combinations are randomly generated, each combination is equivalent to an individual, these individuals are randomly assigned to multiple islands, the population of each island is independently subjected to selection, crossover and mutation operations, and a few elite individuals on each island are periodically allowed to migrate to other islands, thereby gradually iterating to find the global optimal solution.

6. The method for optimizing the structural design of waterproof gaskets based on co-simulation according to claim 5, characterized in that, In the optimization process of the multi-island genetic algorithm, maximizing the average contact stress mises is taken as the optimization objective. The subpopulation size, number of islands, number of generations, total number of iterations, crossover probability and mutation probability are set, and then iterative optimization is performed.

7. A computer device system, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method for optimizing the structural design of waterproof gaskets based on co-simulation, as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for optimizing the structural design of waterproof gaskets based on co-simulation, as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for optimizing the structural design of waterproof gaskets based on co-simulation, as described in any one of claims 1 to 6.

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